Abidan Ailawaer, Yan Wang, Xayda Abduwali, Lei Wang, Ramziya Rifhat
{"title":"Application of change-point analysis to HPV infection and cervical cancer incidence in Xinjiang, China in 2011–2019","authors":"Abidan Ailawaer, Yan Wang, Xayda Abduwali, Lei Wang, Ramziya Rifhat","doi":"10.1186/s13662-024-03823-6","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Objective</h3><p>Cervical cancer (CC), serving as a primary public health challenge, significantly threatens women’s health. However, in terms of change-points, there is still a lack of epidemiological studies on the incidence of HPV infection and CC in Xinjiang,China. This research aims to identify significant changes in the trends of HPV infection and CC prevalence in Xinjiang through change-point analysis (CPA) to provide scientific guidance to health authorities.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>HPV infection and CC time-series data (from January 2011 to December 2019) were collected and analyzed. Meanwhile, their change-points were detected with binary segmentation method and the PELT method. Furthermore, patients were assigned into three groups based on their different ages and subsequently subjected to an analysis employing a segmented regression model (SRM).</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>It was evident that for the monthly HPV time series, the binary segmentation method detected three change points in August 2015, February 2016, and September 2017 (with the most HPV cases). In contrast, the PELT method detected two change-points in September 2015 and April 2017 (with the most HPV cases). For the monthly CC time series, the binary segmentation method identified two change points in October 2012 and August 2019 (with the most CC cases), whereas the PELT method identified three change points in October 2012, August 2019 (with the most CC cases), and October 2019. The SRM demonstrated varying numbers of change points in distinct groups, with HPV infection and CC having the higher growth rate in the 30–49 and 40–59 age groups, respectively. Based on above results, this research was conductive to comprehending the epidemiology of HPV infection and CC in Xinjiang. In addition, it offered scientific guidance for future prevention and management measures for both HPV infection and CC.</p>","PeriodicalId":49245,"journal":{"name":"Advances in Difference Equations","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Difference Equations","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1186/s13662-024-03823-6","RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS","Score":null,"Total":0}
引用次数: 0
Abstract
Objective
Cervical cancer (CC), serving as a primary public health challenge, significantly threatens women’s health. However, in terms of change-points, there is still a lack of epidemiological studies on the incidence of HPV infection and CC in Xinjiang,China. This research aims to identify significant changes in the trends of HPV infection and CC prevalence in Xinjiang through change-point analysis (CPA) to provide scientific guidance to health authorities.
Methods
HPV infection and CC time-series data (from January 2011 to December 2019) were collected and analyzed. Meanwhile, their change-points were detected with binary segmentation method and the PELT method. Furthermore, patients were assigned into three groups based on their different ages and subsequently subjected to an analysis employing a segmented regression model (SRM).
Results
It was evident that for the monthly HPV time series, the binary segmentation method detected three change points in August 2015, February 2016, and September 2017 (with the most HPV cases). In contrast, the PELT method detected two change-points in September 2015 and April 2017 (with the most HPV cases). For the monthly CC time series, the binary segmentation method identified two change points in October 2012 and August 2019 (with the most CC cases), whereas the PELT method identified three change points in October 2012, August 2019 (with the most CC cases), and October 2019. The SRM demonstrated varying numbers of change points in distinct groups, with HPV infection and CC having the higher growth rate in the 30–49 and 40–59 age groups, respectively. Based on above results, this research was conductive to comprehending the epidemiology of HPV infection and CC in Xinjiang. In addition, it offered scientific guidance for future prevention and management measures for both HPV infection and CC.
期刊介绍:
The theory of difference equations, the methods used, and their wide applications have advanced beyond their adolescent stage to occupy a central position in applicable analysis. In fact, in the last 15 years, the proliferation of the subject has been witnessed by hundreds of research articles, several monographs, many international conferences, and numerous special sessions.
The theory of differential and difference equations forms two extreme representations of real world problems. For example, a simple population model when represented as a differential equation shows the good behavior of solutions whereas the corresponding discrete analogue shows the chaotic behavior. The actual behavior of the population is somewhere in between.
The aim of Advances in Difference Equations is to report mainly the new developments in the field of difference equations, and their applications in all fields. We will also consider research articles emphasizing the qualitative behavior of solutions of ordinary, partial, delay, fractional, abstract, stochastic, fuzzy, and set-valued differential equations.
Advances in Difference Equations will accept high-quality articles containing original research results and survey articles of exceptional merit.